semantic segmetation
semantic segmetation use case example
Important
For semantic segmentation dataset, tags
value under visionai
format is mandatory. Thetag
works as the classes information of binary
attributes of RLE
data in objects under frame.
To describe a semantic segmentation dataset with one camera sensor:
sensor: camera (#camera1)
ontology
background
person
bicycle
car
motorcycle
airplane
bus
train
truck
boat
trafficlight
Example Code
{
"visionai": {
"frame_intervals": [
{
"frame_start": 0,
"frame_end": 0
}
],
"frames": {
"000000000000": {
"objects": {
"ad5016f2-9eaa-4ed2-ab0d-0b39833ed7a9": {
"object_data": {
"binary": [
{
"name": "semantic_mask",
"val": "#14463V0#1V1#637V0#11V1#628V0#14V1#624V0#18V1#620V0#23V1#616V0#26V1#613V0#28V1#610V0#30V1#609V0#32V1#608V0#32V1#607V0#34V1#606V0#34V1#605V0#36V1#604V0#36V1#603V0#38V1#602V0#38V1#601V0#40V1#599V0#41V1#598V0#42V1#598V0#42V1#597V0#43V1#595V0#45V1#593V0#47V1#591V0#49V1#589V0#51V1#589V0#51V1#589V0#51V1#589V0#51V1#589V0#51V1#591V0#50V1#591V0#49V1#593V0#47V1#600V0#40V1#600V0#40V1#600V0#40V1#600V0#41V1#599V0#41V1#599V0#41V1#599V0#41V1#599V0#41V1#599V0#41V1#599V0#41V1#600V0#43V1#597V0#47V1#594V0#49V1#591V0#53V1#587V0#55V1#584V0#58V1#580V0#62V1#577V0#65V1#573V0#69V1#570V0#71V1#568V0#74V1#566V0#75V1#564V0#78V1#562V0#79V1#560V0#82V1#558V0#83V1#556V0#86V1#554V0#87V1#552V0#88V1#552V0#89V1#550V0#90V1#550V0#90V1#550V0#90V1#550V0#90V1#550V0#90V1#550V0#90V1#550V0#90V1#550V0#90V1#550V0#90V1#550V0#90V1#550V0#90V1#550V0#90V1#550V0#90V1#550V0#90V1#550V0#90V1#550V0#90V1#550V0#90V1#550V0#91V1#549V0#91V1#549V0#91V1#548V0#92V1#548V0#93V1#547V0#93V1#547V0#93V1#547V0#93V1#547V0#94V1#546V0#94V1#546V0#94V1#546V0#94V1#546V0#95V1#545V0#95V1#545V0#95V1#545V0#95V1#544V0#97V1#543V0#97V1#543V0#97V1#543V0#97V1#543V0#98V1#542V0#98V1#542V0#98V1#542V0#98V1#541V0#99V1#541V0#100V1#540V0#100V1#540V0#100V1#540V0#100V1#540V0#100V1#540V0#100V1#540V0#100V1#539V0#101V1#539V0#100V1#540V0#22V1#1V0#77V1#540V0#22V1#1V0#76V1#541V0#21V1#2V0#76V1#541V0#21V1#2V0#76V1#541V0#21V1#2V0#76V1#541V0#20V1#3V0#77V1#540V0#19V1#4V0#78V1#538V0#20V1#5V0#40V1#18V0#19V1#538V0#19V1#6V0#37V1#27V0#14V1#537V0#19V1#6V0#35V1#30V0#14V1#536V0#19V1#6V0#28V1#38V0#13V1#536V0#18V1#7V0#26V1#41V0#12V1#536V0#18V1#7V0#25V1#43V0#11V1#537V0#17V1#8V0#22V1#46V0#10V1#537V0#16V1#9V0#21V1#47V0#10V1#537V0#16V1#9V0#19V1#50V0#9V1#537V0#15V1#10V0#18V1#51V0#9V1#537V0#15V1#10V0#18V1#52V0#8V1#537V0#14V1#11V0#17V1#53V0#8V1#536V0#15V1#11V0#16V1#55V0#7V1#536V0#14V1#12V0#16V1#55V0#7V1#535V0#14V1#12V0#16V1#57V0#7V1#534V0#12V1#14V0#15V1#58V0#8V1#533V0#11V1#15V0#15V1#58V0#9V1#532V0#8V1#18V0#15V1#57V0#10V1#532V0#8V1#18V0#15V1#57V0#11V1#531V0#8V1#18V0#15V1#57V0#12V1#531V0#8V1#17V0#15V1#56V0#14V1#530V0#10V1#14V0#16V1#56V0#14V1#531V0#10V1#13V0#16V1#56V0#14V1#531V0#11V1#11V0#17V1#55V0#16V1#531V0#10V1#10V0#17V1#56V0#16V1#531V0#10V1#9V0#18V1#56V0#15V1#533V0#9V1#8V0#19V1#56V0#14V1#536V0#6V1#8V0#20V1#56V0#14V1#537V0#5V1#7V0#20V1#56V0#14V1#549V0#21V1#56V0#14V1#548V0#22V1#56V0#14V1#547V0#23V1#56V0#14V1#546V0#24V1#56V0#14V1#545V0#12V1#6V0#7V1#57V0#13V1#545V0#11V1#13V0#1V1#57V0#13V1#544V0#12V1#71V0#13V1#544V0#12V1#71V0#13V1#543V0#13V1#72V0#12V1#543V0#13V1#72V0#12V1#542V0#14V1#626V0#14V1#625V0#14V1#626V0#14V1#626V0#14V1#625V0#15V1#625V0#15V1#625V0#15V1#625V0#17V1#622V0#19V1#621V0#21V1#619V0#23V1#617V0#24V1#615V0#27V1#613V0#27V1#76V0#1V1#536V0#27V1#65V0#12V1#536V0#27V1#64V0#14V1#535V0#27V1#64V0#14V1#535V0#27V1#63V0#15V1#535V0#27V1#63V0#16V1#533V0#27V1#64V0#16V1#533V0#27V1#64V0#16V1#533V0#27V1#64V0#17V1#532V0#27V1#64V0#17V1#532V0#26V1#65V0#18V1#531V0#26V1#65V0#18V1#531V0#25V1#66V0#18V1#531V0#25V1#66V0#19V1#530V0#24V1#67V0#19V1#530V0#24V1#67V0#19V1#530V0#24V1#67V0#19V1#530V0#23V1#68V0#19V1#530V0#23V1#68V0#20V1#529V0#23V1#69V0#19V1#529V0#23V1#69V0#19V1#529V0#22V1#70V0#19V1#529V0#22V1#70V0#19V1#529V0#22V1#70V0#19V1#529V0#20V1#72V0#20V1#529V0#18V1#73V0#20V1#529V0#17V1#74V0#20V1#529V0#17V1#74V0#20V1#529V0#16V1#75V0#20V1#529V0#16V1#75V0#20V1#529V0#16V1#75V0#21V1#528V0#15V1#76V0#21V1#528V0#15V1#76V0#21V1#528V0#15V1#76V0#21V1#528V0#15V1#76V0#22V1#528V0#13V1#76V0#23V1#528V0#13V1#75V0#25V1#527V0#13V1#75V0#25V1#527V0#13V1#74V0#27V1#526V0#12V1#74V0#28V1#526V0#12V1#74V0#28V1#526V0#12V1#75V0#28V1#524V0#13V1#75V0#28V1#524V0#13V1#75V0#29V1#523V0#12V1#76V0#29V1#522V0#13V1#77V0#29V1#521V0#13V1#77V0#29V1#521V0#13V1#77V0#29V1#520V0#13V1#79V0#29V1#519V0#13V1#79V0#29V1#518V0#14V1#79V0#30V1#517V0#14V1#79V0#30V1#516V0#15V1#80V0#30V1#514V0#16V1#80V0#30V1#514V0#16V1#80V0#31V1#512V0#17V1#80V0#31V1#512V0#17V1#81V0#30V1#511V0#18V1#81V0#31V1#510V0#18V1#81V0#31V1#509V0#19V1#81V0#31V1#509V0#19V1#81V0#31V1#508V0#20V1#81V0#32V1#506V0#21V1#81V0#32V1#506V0#21V1#81V0#32V1#505V0#22V1#81V0#32V1#504V0#19V1#85V0#33V1#502V0#16V1#90V0#32V1#501V0#17V1#90V0#32V1#500V0#18V1#91V0#31V1#499V0#20V1#90V0#31V1#499V0#20V1#91V0#30V1#498V0#21V1#91V0#30V1#498V0#21V1#92V0#29V1#497V0#22V1#92V0#29V1#497V0#21V1#96V0#26V1#496V0#21V1#99V0#24V1#497V0#19V1#101V0#23V1#497V0#17V1#104V0#22V1#498V0#15V1#106V0#21V1#502V0#9V1#108V0#21V1#506V0#1V1#113V0#20V1#620V0#20V1#621V0#19V1#621V0#19V1#622V0#18V1#623V0#17V1#623V0#17V1#624V0#16V1#624V0#16V1#625V0#16V1#624V0#16V1#624V0#16V1#624V0#16V1#625V0#15V1#625V0#15V1#625V0#15V1#625V0#15V1#626V0#15V1#625V0#15V1#625V0#15V1#625V0#15V1#625V0#15V1#625V0#15V1#625V0#15V1#625V0#15V1#625V0#16V1#624V0#16V1#624V0#16V1#624V0#16V1#624V0#18V1#623V0#18V1#622V0#20V1#621V0#21V1#620V0#20V1#620V0#21V1#620V0#20V1#620V0#20V1#620V0#19V1#621V0#19V1#622V0#18V1#622V0#18V1#622V0#17V1#623V0#17V1#624V0#16V1#625V0#15V1#626V0#13V1#628V0#12V1#629V0#6V1#23834V0",
"data_type": "",
"encoding": "rle",
"stream": "camera1"
}
]
}
}
},
"frame_properties": {
"streams": {
"camera1": {
"uri": "https://helenmlopsstorageqatest.blob.core.windows.net/vainewformat/coco/segmentations_rle/000000000000/data/camera1/000000000000.jpg"
}
}
}
}
},
"objects": {
"ad5016f2-9eaa-4ed2-ab0d-0b39833ed7a9": {
"name": "segment_mask",
"type": "*segmentation_matrix",
"object_data_pointers": {
"semantic_mask": {
"type": "binary",
"frame_intervals": [
{
"frame_start": 0,
"frame_end": 0
}
]
}
}
}
},
"streams": {
"camera1": {
"type": "camera",
"uri": "",
"description": "Frontal camera"
}
},
"metadata": {
"schema_version": "1.0.0"
},
"tags": {
"6c411fd8-c5c3-4bfa-bb39-d601429c8529": {
"ontology_uid": "",
"type": "semantic_segmentation_RLE",
"tag_data": {
"vec": [
{
"type": "values",
"val": [
"background",
"person",
"bicycle",
"car",
"motorcycle",
"airplane",
"bus",
"train",
"truck",
"boat",
"trafficlight"
],
"name": ""
}
]
}
}
}
}
}
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